Open AccessMulti-lingual Speaker Recognition based on Asymmetric Convolution and Central Loss Function
Author(s) -
Xiaofang Zhang,
Askar Hamdulla,
Mijit Ablimit
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2024/1/012003
Subject(s) - convolution (computer science) , computer science , speech recognition , convolutional neural network , residual neural network , function (biology) , speaker recognition , artificial intelligence , computational complexity theory , pattern recognition (psychology) , algorithm , artificial neural network , evolutionary biology , biology
In order to solve the problem of poor speaker recognition performance on multi-language corpus on convolutional neural networks and large amount of calculations on multiple parameters, this paper draws on asymmetric convolution and center loss (Center Loss) functions to improve and optimize the ResNet model. And perform speaker recognition tasks in the Chinese-Uyghur corpus. The results show that, compared with the original model, the improved model has higher accuracy, fewer parameters, and reduced computational complexity.